Text-to-Image
Diffusers
TensorBoard
Safetensors
StableDiffusionPipeline
dreambooth
diffusers-training
stable-diffusion
stable-diffusion-diffusers
Instructions to use ShadeEngine/SD_kirby_dreambooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShadeEngine/SD_kirby_dreambooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShadeEngine/SD_kirby_dreambooth", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of kirby" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
AuraFlow
AuraFlow is inspired by Stable Diffusion 3 and is by far the largest text-to-image generation model that comes with an Apache 2.0 license. This model achieves state-of-the-art results on the GenEval benchmark.
It was developed by the Fal team and more details about it can be found in this blog post.
AuraFlow can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out this section for more details.
AuraFlowPipeline
[[autodoc]] AuraFlowPipeline - all - call